Native Vector Search. No Elasticsearch Overhead.
Running Elasticsearch and layering vector search on top? JVM tuning, shard strategy, index templates, re-indexing. LanceDB is an AI-native storage engine with native vector search.
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Tomorrow's AI is being built on LanceDB today
Why teams switch
Compute-storage separation
Storage on object storage. Compute scales independently. No JVM clusters.
One table, not six systems
Embeddings, metadata, and raw data together. No Elasticsearch cluster plus lake copy.
Schema evolution without rebuilds
Add columns without re-indexing indexes or rebalancing shards.
Full-text + hybrid search, native
Vector, full-text, and SQL queries in one system. Not kNN bolted onto Lucene.
Comparison
Elasticsearch
LanceDB
Cost
JVM clusters for peak load. 24/7 node costs.
Object storage with compute-storage separation. Up to 100x savings.
Scale
Scale by adding nodes. Shard management required.
20 PB largest table. 20K+ QPS. Billions of vectors.
Search
Full-text native. Vector via kNN.
Native vector, full-text, and SQL hybrid search in one query.
Data model
Index-centric. Vectors inherit cluster behavior.
Raw data, embeddings, and features in one table.
Purpose
Text/log search with vector features.
Built for vector and AI workloads.
Best for
Text search with some vector.
Vector-first workloads at scale.
The Power of the Lance Format
Vector Search
- Fast scans and random access from the same table — no tradeoff
- Zero-copy access for high throughput without serialization overhead
Multi-Modal
- Raw data, embeddings, and metadata in one table — not pointers to blob storage
- No separate metadata store to keep in sync
Enterprise-Grade Requirements
Security
Granular RBAC, SSO integration, and VPC deployment options.
Governance
Data versioning and time-travel capabilities for auditability.
Support
Dedicated technical account management and guaranteed SLAs.
Talk to Engineering
Or try LanceDB OSS — same code, scales to Cloud.
Schedule a Technical Consultation